The California Wine Growers Association held a wine quality contest. They
accepted entries from the entire state and gave individual prizes, but they
also decided to give prizes for the regions with the best wine. They
divided the state into three regions: North, Napa, and Central
and collected the data from the individual entries. This data
is contained in the file wine.mtw
They wish to give prizes to each region with the best Aroma, Flavor,
and Quality and they
have hired you to decide who gets the prizes. The association wishes to give
the prize to the region that has the best overall characteristic rather than
a region that has one wine with high
marks and the rest with low marks. For their own information, they
also wish to determine
which of the factors: clarity, aroma, body, flavor, and oakiness is the best
predictor of the quality.

Data:

The judges gave marks for the individual wines. The data file is set up as follows:

Column

C1

C2

C3

C4

C5

C6

C7

Data

Clarity

Aroma

Body

Flavor

Oakiness

Quality

Region

The regions are 1 - North, 2 - Central, 3 - Napa

Open Minitab 10.5 from the apple menu, under Math1010Apps.

Open Worksheet from the File menu, then open
the wine.mtw
from the Places/Applications HD/Applications/Minitab/data directory
.

Prizes
Explore the graphical representation
that would be best for awarding each prize: mean,
median, mode, histograms, side-by-side boxplots, or
other minitab graphs. The association would
like to be convinced that one region deserves the prize over the others.
Copy and paste the graphs you decided were best into your report.
You should give your prize recommendations.
Explain who is first, second, and third
and justify your ranking from your graphs
in a way that the association can understand.

Predictor of Quality
Use linear regression to answer this question. Paste your graphs into
Word, and also note the r^2 value.
Then answer their questions concerning the predictability of
quality and explain the equation and the r-squared value.
Give an example of using your line to make a
prediction and explain how good you expect the prediction to be.